Digital cameras and scanners are used for imaging objects. The resulting image of the object can then be image processed for a variety of reasons.
One very important image processing that takes place following the capture of an object image is for determining note authenticity. Automated Teller Machines (ATMs) and vending machines that accept cash for deposit or for payment of vended goods must determine if the note being provided by a consumer is genuine in a relatively short period of time.
In-line imaging scanners, along the note transport path within the machine, typically have a fixed position for capturing the image of a note. If the scanner lens has a small amount of debris or if the note being imaged is not properly centered, some portions of the image can become distorted. The determination as to whether a note is genuine or not relies on the pixel intensities in the note image, such that when the note is not properly illuminated, centered, or when a portion of the lens has debris, then some parts of the note image will have incorrect pixel intensities, which will alter the determination of whether the note is genuine or not.
Moreover, any resulting non-uniformly illuminated image degrades the algorithm performance in discriminating between a genuine note and a non-genuine note. As a consequence, a trade-off has to be made in these situations between false retention of genuine notes and false acceptance of non-genuine notes.
This is not an ideal situation for vending machines accepting currency as payment for vended goods and very problematic for ATMs that can handle large amounts of currency in any given day.
Processing throughput is a concern but so is getting the note's authenticity correct because errors in the favor of a customer can be costly to the enterprise and errors in favor of the enterprise can result in manual enterprise exception procedures with customer complaints and frustration.